Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations32833
Missing cells5673
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.4 MiB
Average record size in memory714.7 B

Variable types

Text7
Numeric14
DateTime1
Categorical3

Alerts

energy is highly overall correlated with loudnessHigh correlation
loudness is highly overall correlated with energyHigh correlation
playlist_genre is highly overall correlated with playlist_subgenreHigh correlation
playlist_subgenre is highly overall correlated with playlist_genreHigh correlation
track_album_release_date has 1886 (5.7%) missing valuesMissing
release_year has 1886 (5.7%) missing valuesMissing
release_month has 1886 (5.7%) missing valuesMissing
track_popularity has 2703 (8.2%) zerosZeros
key has 3454 (10.5%) zerosZeros
instrumentalness has 12089 (36.8%) zerosZeros

Reproduction

Analysis started2025-11-08 09:11:31.992056
Analysis finished2025-11-08 09:12:07.380550
Duration35.39 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct28356
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2025-11-08T09:12:07.654460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters722326
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25190 ?
Unique (%)76.7%

Sample

1st row6f807x0ima9a1j3VPbc7VN
2nd row0r7CVbZTWZgbTCYdfa2P31
3rd row1z1Hg7Vb0AhHDiEmnDE79l
4th row75FpbthrwQmzHlBJLuGdC7
5th row1e8PAfcKUYoKkxPhrHqw4x
ValueCountFrequency (%)
7bklcz1jbubvqri2fvltvw10
 
< 0.1%
14sos5l36385fj3ol8hew49
 
< 0.1%
3eekarcy7kvn4yt5zfzltw9
 
< 0.1%
7h0d2h0fumzbs7zefigjpn8
 
< 0.1%
6gg1gjgki2ak4e0qzsr7sd8
 
< 0.1%
6oj6le65b3seqpwmrnxwjy8
 
< 0.1%
2tnvg71enuj33ic2nfn6kz8
 
< 0.1%
0qawevpkts34wf68r8dzx98
 
< 0.1%
7lzouawgfcy4tkxdooneym8
 
< 0.1%
2fxmhks0bxgsbdj92vm42m8
 
< 0.1%
Other values (28346)32749
99.7%
2025-11-08T09:12:08.040180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
015445
 
2.1%
615375
 
2.1%
315304
 
2.1%
415295
 
2.1%
215212
 
2.1%
115190
 
2.1%
515170
 
2.1%
714649
 
2.0%
C11345
 
1.6%
R11338
 
1.6%
Other values (52)578003
80.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)722326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
015445
 
2.1%
615375
 
2.1%
315304
 
2.1%
415295
 
2.1%
215212
 
2.1%
115190
 
2.1%
515170
 
2.1%
714649
 
2.0%
C11345
 
1.6%
R11338
 
1.6%
Other values (52)578003
80.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)722326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
015445
 
2.1%
615375
 
2.1%
315304
 
2.1%
415295
 
2.1%
215212
 
2.1%
115190
 
2.1%
515170
 
2.1%
714649
 
2.0%
C11345
 
1.6%
R11338
 
1.6%
Other values (52)578003
80.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)722326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
015445
 
2.1%
615375
 
2.1%
315304
 
2.1%
415295
 
2.1%
215212
 
2.1%
115190
 
2.1%
515170
 
2.1%
714649
 
2.0%
C11345
 
1.6%
R11338
 
1.6%
Other values (52)578003
80.0%
Distinct22801
Distinct (%)69.5%
Missing5
Missing (%)< 0.1%
Memory size2.0 MiB
2025-11-08T09:12:08.457613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length133
Median length86
Mean length16.253412
Min length0

Characters and Unicode

Total characters533567
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17686 ?
Unique (%)53.9%

Sample

1st rowi dont care with justin bieber loud luxury remix
2nd rowmemories dillon francis remix
3rd rowall the time don diablo remix
4th rowcall you mine keanu silva remix
5th rowsomeone you loved future humans remix
ValueCountFrequency (%)
feat2804
 
2.7%
the2681
 
2.6%
remix2110
 
2.1%
you1881
 
1.8%
me1631
 
1.6%
i1163
 
1.1%
love1136
 
1.1%
a852
 
0.8%
to845
 
0.8%
my837
 
0.8%
Other values (14986)86908
84.5%
2025-11-08T09:12:09.018404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70188
13.2%
e54999
 
10.3%
a40445
 
7.6%
o37220
 
7.0%
i35101
 
6.6%
t33249
 
6.2%
r29609
 
5.5%
n28561
 
5.4%
s24225
 
4.5%
l23295
 
4.4%
Other values (27)156675
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)533567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
70188
13.2%
e54999
 
10.3%
a40445
 
7.6%
o37220
 
7.0%
i35101
 
6.6%
t33249
 
6.2%
r29609
 
5.5%
n28561
 
5.4%
s24225
 
4.5%
l23295
 
4.4%
Other values (27)156675
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)533567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
70188
13.2%
e54999
 
10.3%
a40445
 
7.6%
o37220
 
7.0%
i35101
 
6.6%
t33249
 
6.2%
r29609
 
5.5%
n28561
 
5.4%
s24225
 
4.5%
l23295
 
4.4%
Other values (27)156675
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)533567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
70188
13.2%
e54999
 
10.3%
a40445
 
7.6%
o37220
 
7.0%
i35101
 
6.6%
t33249
 
6.2%
r29609
 
5.5%
n28561
 
5.4%
s24225
 
4.5%
l23295
 
4.4%
Other values (27)156675
29.4%
Distinct4551
Distinct (%)13.9%
Missing5
Missing (%)< 0.1%
Memory size1.8 MiB
2025-11-08T09:12:09.363336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length31
Mean length8.9137931
Min length0

Characters and Unicode

Total characters292622
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowed sheeran
2nd rowmaroon 5
3rd rowzara larsson
4th rowthe chainsmokers
5th rowlewis capaldi
ValueCountFrequency (%)
other6101
 
11.6%
the1488
 
2.8%
martin220
 
0.4%
mike208
 
0.4%
dj197
 
0.4%
lil195
 
0.4%
j174
 
0.3%
david166
 
0.3%
garrix161
 
0.3%
john147
 
0.3%
Other values (5577)43741
82.8%
2025-11-08T09:12:09.839873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e31310
 
10.7%
a24697
 
8.4%
o22621
 
7.7%
r22374
 
7.6%
20049
 
6.9%
t19017
 
6.5%
n17118
 
5.8%
i16806
 
5.7%
h14260
 
4.9%
l14077
 
4.8%
Other values (27)90293
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)292622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e31310
 
10.7%
a24697
 
8.4%
o22621
 
7.7%
r22374
 
7.6%
20049
 
6.9%
t19017
 
6.5%
n17118
 
5.8%
i16806
 
5.7%
h14260
 
4.9%
l14077
 
4.8%
Other values (27)90293
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)292622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e31310
 
10.7%
a24697
 
8.4%
o22621
 
7.7%
r22374
 
7.6%
20049
 
6.9%
t19017
 
6.5%
n17118
 
5.8%
i16806
 
5.7%
h14260
 
4.9%
l14077
 
4.8%
Other values (27)90293
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)292622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e31310
 
10.7%
a24697
 
8.4%
o22621
 
7.7%
r22374
 
7.6%
20049
 
6.9%
t19017
 
6.5%
n17118
 
5.8%
i16806
 
5.7%
h14260
 
4.9%
l14077
 
4.8%
Other values (27)90293
30.9%

track_popularity
Real number (ℝ)

Zeros 

Distinct101
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.477081
Minimum0
Maximum100
Zeros2703
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2025-11-08T09:12:09.981822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124
median45
Q362
95-th percentile79
Maximum100
Range100
Interquartile range (IQR)38

Descriptive statistics

Standard deviation24.984074
Coefficient of variation (CV)0.58817776
Kurtosis-0.93277039
Mean42.477081
Median Absolute Deviation (MAD)18
Skewness-0.23332007
Sum1394650
Variance624.20398
MonotonicityNot monotonic
2025-11-08T09:12:10.132381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02703
 
8.2%
1575
 
1.8%
57541
 
1.6%
51514
 
1.6%
60514
 
1.6%
54514
 
1.6%
52506
 
1.5%
45505
 
1.5%
58503
 
1.5%
50498
 
1.5%
Other values (91)25460
77.5%
ValueCountFrequency (%)
02703
8.2%
1575
 
1.8%
2387
 
1.2%
3321
 
1.0%
4240
 
0.7%
5240
 
0.7%
6192
 
0.6%
7189
 
0.6%
8201
 
0.6%
9195
 
0.6%
ValueCountFrequency (%)
1002
 
< 0.1%
994
 
< 0.1%
9836
0.1%
9722
 
0.1%
967
 
< 0.1%
9515
 
< 0.1%
9437
0.1%
9344
0.1%
9227
0.1%
9158
0.2%
Distinct22545
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2025-11-08T09:12:10.475189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters722326
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17545 ?
Unique (%)53.4%

Sample

1st row2oCs0DGTsRO98Gh5ZSl2Cx
2nd row63rPSO264uRjW1X5E6cWv6
3rd row1HoSmj2eLcsrR0vE9gThr4
4th row1nqYsOef1yKKuGOVchbsk6
5th row7m7vv9wlQ4i0LFuJiE2zsQ
ValueCountFrequency (%)
5l1xcowsxwzfusjzvymp4842
 
0.1%
5fstcqs5npilf42vhpnv2329
 
0.1%
7cjjb2mikwawa1v6kewfbf28
 
0.1%
4vfg1doutedmbjblzt7hck26
 
0.1%
2htbq0rhwukkvxaltmczp221
 
0.1%
4czt5uefbrpbilw34hqyxi21
 
0.1%
5xcotqg63v60ns82pmqmbe20
 
0.1%
6ylffzx32icw4l1a7ywnln20
 
0.1%
246e5ovv4qxhprgosj7vdb20
 
0.1%
0s0kgznfbgsissff54wsjh18
 
0.1%
Other values (22535)32588
99.3%
2025-11-08T09:12:10.889352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
115957
 
2.2%
215554
 
2.2%
015513
 
2.1%
515509
 
2.1%
315467
 
2.1%
615368
 
2.1%
415075
 
2.1%
714325
 
2.0%
w11443
 
1.6%
e11355
 
1.6%
Other values (52)576760
79.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)722326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
115957
 
2.2%
215554
 
2.2%
015513
 
2.1%
515509
 
2.1%
315467
 
2.1%
615368
 
2.1%
415075
 
2.1%
714325
 
2.0%
w11443
 
1.6%
e11355
 
1.6%
Other values (52)576760
79.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)722326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
115957
 
2.2%
215554
 
2.2%
015513
 
2.1%
515509
 
2.1%
315467
 
2.1%
615368
 
2.1%
415075
 
2.1%
714325
 
2.0%
w11443
 
1.6%
e11355
 
1.6%
Other values (52)576760
79.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)722326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
115957
 
2.2%
215554
 
2.2%
015513
 
2.1%
515509
 
2.1%
315467
 
2.1%
615368
 
2.1%
415075
 
2.1%
714325
 
2.0%
w11443
 
1.6%
e11355
 
1.6%
Other values (52)576760
79.8%
Distinct19331
Distinct (%)58.9%
Missing5
Missing (%)< 0.1%
Memory size2.1 MiB
2025-11-08T09:12:11.241378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length144
Median length92
Mean length16.608018
Min length0

Characters and Unicode

Total characters545208
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13749 ?
Unique (%)41.9%

Sample

1st rowi dont care with justin bieber loud luxury remix
2nd rowmemories dillon francis remix
3rd rowall the time don diablo remix
4th rowcall you mine the remixes
5th rowsomeone you loved future humans remix
ValueCountFrequency (%)
the4567
 
4.6%
of1577
 
1.6%
feat1398
 
1.4%
remix1119
 
1.1%
you1064
 
1.1%
me931
 
0.9%
deluxe914
 
0.9%
a839
 
0.8%
love796
 
0.8%
to787
 
0.8%
Other values (13986)86029
86.0%
2025-11-08T09:12:11.775288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67315
12.3%
e59701
 
11.0%
a38091
 
7.0%
o36802
 
6.8%
t36114
 
6.6%
i35286
 
6.5%
r30490
 
5.6%
n29069
 
5.3%
s28758
 
5.3%
l24585
 
4.5%
Other values (27)158997
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)545208
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
67315
12.3%
e59701
 
11.0%
a38091
 
7.0%
o36802
 
6.8%
t36114
 
6.6%
i35286
 
6.5%
r30490
 
5.6%
n29069
 
5.3%
s28758
 
5.3%
l24585
 
4.5%
Other values (27)158997
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)545208
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
67315
12.3%
e59701
 
11.0%
a38091
 
7.0%
o36802
 
6.8%
t36114
 
6.6%
i35286
 
6.5%
r30490
 
5.6%
n29069
 
5.3%
s28758
 
5.3%
l24585
 
4.5%
Other values (27)158997
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)545208
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
67315
12.3%
e59701
 
11.0%
a38091
 
7.0%
o36802
 
6.8%
t36114
 
6.6%
i35286
 
6.5%
r30490
 
5.6%
n29069
 
5.3%
s28758
 
5.3%
l24585
 
4.5%
Other values (27)158997
29.2%
Distinct4453
Distinct (%)14.4%
Missing1886
Missing (%)5.7%
Memory size256.6 KiB
Minimum1957-01-01 00:00:00
Maximum2020-01-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-08T09:12:11.912481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:12:12.073942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct312
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2025-11-08T09:12:12.466025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length105
Median length64
Mean length21.141321
Min length5

Characters and Unicode

Total characters694133
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpop remix
2nd rowpop remix
3rd rowpop remix
4th rowpop remix
5th rowpop remix
ValueCountFrequency (%)
pop4490
 
3.9%
rock4267
 
3.7%
other3606
 
3.1%
house3202
 
2.8%
20202359
 
2.0%
hip2255
 
2.0%
hits2173
 
1.9%
hop2063
 
1.8%
rap2003
 
1.7%
edm1904
 
1.7%
Other values (449)87025
75.4%
2025-11-08T09:12:12.986216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
82514
 
11.9%
o57393
 
8.3%
e57038
 
8.2%
s43514
 
6.3%
r41953
 
6.0%
a40752
 
5.9%
t39888
 
5.7%
p37875
 
5.5%
i37739
 
5.4%
n31418
 
4.5%
Other values (27)224049
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)694133
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
82514
 
11.9%
o57393
 
8.3%
e57038
 
8.2%
s43514
 
6.3%
r41953
 
6.0%
a40752
 
5.9%
t39888
 
5.7%
p37875
 
5.5%
i37739
 
5.4%
n31418
 
4.5%
Other values (27)224049
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)694133
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
82514
 
11.9%
o57393
 
8.3%
e57038
 
8.2%
s43514
 
6.3%
r41953
 
6.0%
a40752
 
5.9%
t39888
 
5.7%
p37875
 
5.5%
i37739
 
5.4%
n31418
 
4.5%
Other values (27)224049
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)694133
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
82514
 
11.9%
o57393
 
8.3%
e57038
 
8.2%
s43514
 
6.3%
r41953
 
6.0%
a40752
 
5.9%
t39888
 
5.7%
p37875
 
5.5%
i37739
 
5.4%
n31418
 
4.5%
Other values (27)224049
32.3%
Distinct471
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2025-11-08T09:12:13.241496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters722326
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row37i9dQZF1DXcZDD7cfEKhW
2nd row37i9dQZF1DXcZDD7cfEKhW
3rd row37i9dQZF1DXcZDD7cfEKhW
4th row37i9dQZF1DXcZDD7cfEKhW
5th row37i9dQZF1DXcZDD7cfEKhW
ValueCountFrequency (%)
4jkkvmpvl4lsioqqjeal0q247
 
0.8%
37i9dqzf1dwthm4kx49uks198
 
0.6%
6knqdwp0syvhfhor4lwp7x195
 
0.6%
3xmqtdloigvj3lwh5e5x6f189
 
0.6%
3ho3io0ijykgeqnbjb2sic182
 
0.6%
25butzrvb1zj1mjioms09d109
 
0.3%
37i9dqzf1dx3fxjqxgjuep100
 
0.3%
37i9dqzf1dx0amssoukcz7100
 
0.3%
0ncspsyf0os4bspgghkqxm100
 
0.3%
28ke4pxwqf2a5b2gfq1pqt100
 
0.3%
Other values (461)31313
95.4%
2025-11-08T09:12:13.631554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
319178
 
2.7%
118762
 
2.6%
718699
 
2.6%
Q18247
 
2.5%
D17097
 
2.4%
i17039
 
2.4%
d16004
 
2.2%
915566
 
2.2%
Z15453
 
2.1%
F15033
 
2.1%
Other values (52)551248
76.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)722326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
319178
 
2.7%
118762
 
2.6%
718699
 
2.6%
Q18247
 
2.5%
D17097
 
2.4%
i17039
 
2.4%
d16004
 
2.2%
915566
 
2.2%
Z15453
 
2.1%
F15033
 
2.1%
Other values (52)551248
76.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)722326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
319178
 
2.7%
118762
 
2.6%
718699
 
2.6%
Q18247
 
2.5%
D17097
 
2.4%
i17039
 
2.4%
d16004
 
2.2%
915566
 
2.2%
Z15453
 
2.1%
F15033
 
2.1%
Other values (52)551248
76.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)722326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
319178
 
2.7%
118762
 
2.6%
718699
 
2.6%
Q18247
 
2.5%
D17097
 
2.4%
i17039
 
2.4%
d16004
 
2.2%
915566
 
2.2%
Z15453
 
2.1%
F15033
 
2.1%
Other values (52)551248
76.3%

playlist_genre
Categorical

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
edm
6043 
rap
5746 
pop
5507 
rb
5431 
latin
5155 

Length

Max length5
Median length3
Mean length3.2993939
Min length2

Characters and Unicode

Total characters108329
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpop
2nd rowpop
3rd rowpop
4th rowpop
5th rowpop

Common Values

ValueCountFrequency (%)
edm6043
18.4%
rap5746
17.5%
pop5507
16.8%
rb5431
16.5%
latin5155
15.7%
rock4951
15.1%

Length

2025-11-08T09:12:13.767794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T09:12:13.864401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
edm6043
18.4%
rap5746
17.5%
pop5507
16.8%
rb5431
16.5%
latin5155
15.7%
rock4951
15.1%

Most occurring characters

ValueCountFrequency (%)
p16760
15.5%
r16128
14.9%
a10901
10.1%
o10458
9.7%
d6043
 
5.6%
e6043
 
5.6%
m6043
 
5.6%
b5431
 
5.0%
l5155
 
4.8%
t5155
 
4.8%
Other values (4)20212
18.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)108329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p16760
15.5%
r16128
14.9%
a10901
10.1%
o10458
9.7%
d6043
 
5.6%
e6043
 
5.6%
m6043
 
5.6%
b5431
 
5.0%
l5155
 
4.8%
t5155
 
4.8%
Other values (4)20212
18.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)108329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p16760
15.5%
r16128
14.9%
a10901
10.1%
o10458
9.7%
d6043
 
5.6%
e6043
 
5.6%
m6043
 
5.6%
b5431
 
5.0%
l5155
 
4.8%
t5155
 
4.8%
Other values (4)20212
18.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)108329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p16760
15.5%
r16128
14.9%
a10901
10.1%
o10458
9.7%
d6043
 
5.6%
e6043
 
5.6%
m6043
 
5.6%
b5431
 
5.0%
l5155
 
4.8%
t5155
 
4.8%
Other values (4)20212
18.7%

playlist_subgenre
Categorical

High correlation 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
progressive electro house
 
1809
southern hip hop
 
1675
indie poptimism
 
1672
latin hip hop
 
1656
neo soul
 
1637
Other values (19)
24384 

Length

Max length25
Median length15
Mean length11.514239
Min length4

Characters and Unicode

Total characters378047
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdance pop
2nd rowdance pop
3rd rowdance pop
4th rowdance pop
5th rowdance pop

Common Values

ValueCountFrequency (%)
progressive electro house1809
 
5.5%
southern hip hop1675
 
5.1%
indie poptimism1672
 
5.1%
latin hip hop1656
 
5.0%
neo soul1637
 
5.0%
pop edm1517
 
4.6%
electro house1511
 
4.6%
hard rock1485
 
4.5%
gangster rap1458
 
4.4%
electropop1408
 
4.3%
Other values (14)17005
51.8%

Length

2025-11-08T09:12:13.993458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pop6462
 
9.6%
hip5909
 
8.8%
hop4653
 
6.9%
rock3846
 
5.7%
house3320
 
5.0%
electro3320
 
5.0%
latin2918
 
4.4%
progressive1809
 
2.7%
southern1675
 
2.5%
indie1672
 
2.5%
Other values (24)31419
46.9%

Most occurring characters

ValueCountFrequency (%)
o41435
11.0%
p39131
10.4%
e35660
 
9.4%
34170
 
9.0%
r28322
 
7.5%
i22247
 
5.9%
t20747
 
5.5%
a20659
 
5.5%
n20022
 
5.3%
s18234
 
4.8%
Other values (13)97420
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)378047
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o41435
11.0%
p39131
10.4%
e35660
 
9.4%
34170
 
9.0%
r28322
 
7.5%
i22247
 
5.9%
t20747
 
5.5%
a20659
 
5.5%
n20022
 
5.3%
s18234
 
4.8%
Other values (13)97420
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)378047
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o41435
11.0%
p39131
10.4%
e35660
 
9.4%
34170
 
9.0%
r28322
 
7.5%
i22247
 
5.9%
t20747
 
5.5%
a20659
 
5.5%
n20022
 
5.3%
s18234
 
4.8%
Other values (13)97420
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)378047
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o41435
11.0%
p39131
10.4%
e35660
 
9.4%
34170
 
9.0%
r28322
 
7.5%
i22247
 
5.9%
t20747
 
5.5%
a20659
 
5.5%
n20022
 
5.3%
s18234
 
4.8%
Other values (13)97420
25.8%

danceability
Real number (ℝ)

Distinct822
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65484952
Minimum0
Maximum0.983
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2025-11-08T09:12:14.115285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.392
Q10.563
median0.672
Q30.761
95-th percentile0.868
Maximum0.983
Range0.983
Interquartile range (IQR)0.198

Descriptive statistics

Standard deviation0.14508532
Coefficient of variation (CV)0.22155521
Kurtosis0.010202119
Mean0.65484952
Median Absolute Deviation (MAD)0.098
Skewness-0.50448844
Sum21500.674
Variance0.02104975
MonotonicityNot monotonic
2025-11-08T09:12:14.266580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.733118
 
0.4%
0.708115
 
0.4%
0.704112
 
0.3%
0.694112
 
0.3%
0.784111
 
0.3%
0.701111
 
0.3%
0.69111
 
0.3%
0.655110
 
0.3%
0.676110
 
0.3%
0.689109
 
0.3%
Other values (812)31714
96.6%
ValueCountFrequency (%)
01
< 0.1%
0.07711
< 0.1%
0.07871
< 0.1%
0.09851
< 0.1%
0.1161
< 0.1%
0.1181
< 0.1%
0.131
< 0.1%
0.1352
< 0.1%
0.142
< 0.1%
0.1411
< 0.1%
ValueCountFrequency (%)
0.9831
 
< 0.1%
0.9811
 
< 0.1%
0.9792
 
< 0.1%
0.9781
 
< 0.1%
0.9771
 
< 0.1%
0.9752
 
< 0.1%
0.9745
< 0.1%
0.9734
< 0.1%
0.9722
 
< 0.1%
0.9712
 
< 0.1%

energy
Real number (ℝ)

High correlation 

Distinct952
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69861927
Minimum0.000175
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2025-11-08T09:12:14.449968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.000175
5-th percentile0.366
Q10.581
median0.721
Q30.84
95-th percentile0.949
Maximum1
Range0.999825
Interquartile range (IQR)0.259

Descriptive statistics

Standard deviation0.18091003
Coefficient of variation (CV)0.25895368
Kurtosis0.000528152
Mean0.69861927
Median Absolute Deviation (MAD)0.128
Skewness-0.63632984
Sum22937.767
Variance0.03272844
MonotonicityNot monotonic
2025-11-08T09:12:14.608040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.787100
 
0.3%
0.82899
 
0.3%
0.83398
 
0.3%
0.72691
 
0.3%
0.79591
 
0.3%
0.71191
 
0.3%
0.86989
 
0.3%
0.75889
 
0.3%
0.7688
 
0.3%
0.88787
 
0.3%
Other values (942)31910
97.2%
ValueCountFrequency (%)
0.0001751
< 0.1%
0.008141
< 0.1%
0.01181
< 0.1%
0.01611
< 0.1%
0.01671
< 0.1%
0.02861
< 0.1%
0.02971
< 0.1%
0.03231
< 0.1%
0.0361
< 0.1%
0.03751
< 0.1%
ValueCountFrequency (%)
13
 
< 0.1%
0.9997
 
< 0.1%
0.9985
 
< 0.1%
0.9976
 
< 0.1%
0.99610
 
< 0.1%
0.99513
< 0.1%
0.99411
 
< 0.1%
0.99330
0.1%
0.99217
0.1%
0.99120
0.1%

key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3744708
Minimum0
Maximum11
Zeros3454
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2025-11-08T09:12:14.754626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.6116574
Coefficient of variation (CV)0.67200242
Kurtosis-1.307069
Mean5.3744708
Median Absolute Deviation (MAD)3
Skewness-0.023909144
Sum176460
Variance13.044069
MonotonicityNot monotonic
2025-11-08T09:12:14.844558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
14010
12.2%
03454
10.5%
73352
10.2%
93027
9.2%
112996
9.1%
22827
8.6%
52680
8.2%
62670
8.1%
82430
7.4%
102273
6.9%
Other values (2)3114
9.5%
ValueCountFrequency (%)
03454
10.5%
14010
12.2%
22827
8.6%
3913
 
2.8%
42201
6.7%
52680
8.2%
62670
8.1%
73352
10.2%
82430
7.4%
93027
9.2%
ValueCountFrequency (%)
112996
9.1%
102273
6.9%
93027
9.2%
82430
7.4%
73352
10.2%
62670
8.1%
52680
8.2%
42201
6.7%
3913
 
2.8%
22827
8.6%

loudness
Real number (ℝ)

High correlation 

Distinct10222
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.7194991
Minimum-46.448
Maximum1.275
Zeros0
Zeros (%)0.0%
Negative32827
Negative (%)> 99.9%
Memory size256.6 KiB
2025-11-08T09:12:14.965279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-46.448
5-th percentile-12.4502
Q1-8.171
median-6.166
Q3-4.645
95-th percentile-2.972
Maximum1.275
Range47.723
Interquartile range (IQR)3.526

Descriptive statistics

Standard deviation2.9884364
Coefficient of variation (CV)-0.44474094
Kurtosis4.4909579
Mean-6.7194991
Median Absolute Deviation (MAD)1.703
Skewness-1.364097
Sum-220621.32
Variance8.930752
MonotonicityNot monotonic
2025-11-08T09:12:15.196971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.44320
 
0.1%
-5.60820
 
0.1%
-4.97320
 
0.1%
-3.78220
 
0.1%
-6.420
 
0.1%
-5.57618
 
0.1%
-4.57618
 
0.1%
-6.40618
 
0.1%
-5.04118
 
0.1%
-6.55416
 
< 0.1%
Other values (10212)32645
99.4%
ValueCountFrequency (%)
-46.4481
< 0.1%
-36.6241
< 0.1%
-36.5091
< 0.1%
-35.961
< 0.1%
-35.4271
< 0.1%
-34.2831
< 0.1%
-29.5611
< 0.1%
-28.3091
< 0.1%
-26.2791
< 0.1%
-26.2071
< 0.1%
ValueCountFrequency (%)
1.2751
< 0.1%
1.1351
< 0.1%
0.6421
< 0.1%
0.5511
< 0.1%
0.3261
< 0.1%
0.3021
< 0.1%
-0.0461
< 0.1%
-0.0731
< 0.1%
-0.1551
< 0.1%
-0.1581
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
1
18574 
0
14259 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters32833
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
118574
56.6%
014259
43.4%

Length

2025-11-08T09:12:15.393653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-08T09:12:15.518517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
118574
56.6%
014259
43.4%

Most occurring characters

ValueCountFrequency (%)
118574
56.6%
014259
43.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)32833
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
118574
56.6%
014259
43.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)32833
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
118574
56.6%
014259
43.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)32833
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
118574
56.6%
014259
43.4%

speechiness
Real number (ℝ)

Distinct1270
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10706807
Minimum0
Maximum0.918
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2025-11-08T09:12:15.677779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0298
Q10.041
median0.0625
Q30.132
95-th percentile0.3324
Maximum0.918
Range0.918
Interquartile range (IQR)0.091

Descriptive statistics

Standard deviation0.10131413
Coefficient of variation (CV)0.94625907
Kurtosis4.2608346
Mean0.10706807
Median Absolute Deviation (MAD)0.0274
Skewness1.9670285
Sum3515.3659
Variance0.010264553
MonotonicityNot monotonic
2025-11-08T09:12:15.878269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.102116
 
0.4%
0.10398
 
0.3%
0.10993
 
0.3%
0.035493
 
0.3%
0.11289
 
0.3%
0.034688
 
0.3%
0.10788
 
0.3%
0.12387
 
0.3%
0.10685
 
0.3%
0.036385
 
0.3%
Other values (1260)31911
97.2%
ValueCountFrequency (%)
01
 
< 0.1%
0.02242
 
< 0.1%
0.02251
 
< 0.1%
0.02285
< 0.1%
0.0231
 
< 0.1%
0.02311
 
< 0.1%
0.02324
< 0.1%
0.02332
 
< 0.1%
0.02343
< 0.1%
0.02356
< 0.1%
ValueCountFrequency (%)
0.9181
< 0.1%
0.8771
< 0.1%
0.8692
< 0.1%
0.8651
< 0.1%
0.861
< 0.1%
0.8561
< 0.1%
0.8551
< 0.1%
0.8531
< 0.1%
0.8171
< 0.1%
0.7921
< 0.1%

acousticness
Real number (ℝ)

Distinct3731
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17533372
Minimum0
Maximum0.994
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2025-11-08T09:12:16.072575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0006306
Q10.0151
median0.0804
Q30.255
95-th percentile0.682
Maximum0.994
Range0.994
Interquartile range (IQR)0.2399

Descriptive statistics

Standard deviation0.21963254
Coefficient of variation (CV)1.2526543
Kurtosis1.8784089
Mean0.17533372
Median Absolute Deviation (MAD)0.07623
Skewness1.5947859
Sum5756.7319
Variance0.048238453
MonotonicityNot monotonic
2025-11-08T09:12:16.289360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10280
 
0.2%
0.12880
 
0.2%
0.10178
 
0.2%
0.11474
 
0.2%
0.14171
 
0.2%
0.10770
 
0.2%
0.12569
 
0.2%
0.10469
 
0.2%
0.12268
 
0.2%
0.1165
 
0.2%
Other values (3721)32109
97.8%
ValueCountFrequency (%)
01
< 0.1%
1.4 × 10-61
< 0.1%
1.44 × 10-61
< 0.1%
1.47 × 10-61
< 0.1%
1.66 × 10-61
< 0.1%
2.16 × 10-61
< 0.1%
2.22 × 10-61
< 0.1%
2.32 × 10-61
< 0.1%
2.43 × 10-61
< 0.1%
2.46 × 10-61
< 0.1%
ValueCountFrequency (%)
0.9941
 
< 0.1%
0.9921
 
< 0.1%
0.9893
< 0.1%
0.9862
< 0.1%
0.9852
< 0.1%
0.9842
< 0.1%
0.9833
< 0.1%
0.9821
 
< 0.1%
0.9794
< 0.1%
0.9783
< 0.1%

instrumentalness
Real number (ℝ)

Zeros 

Distinct4729
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.084747161
Minimum0
Maximum0.994
Zeros12089
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2025-11-08T09:12:16.548087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.61 × 10-5
Q30.00483
95-th percentile0.767
Maximum0.994
Range0.994
Interquartile range (IQR)0.00483

Descriptive statistics

Standard deviation0.22423012
Coefficient of variation (CV)2.6458718
Kurtosis6.2740615
Mean0.084747161
Median Absolute Deviation (MAD)1.61 × 10-5
Skewness2.7594718
Sum2782.5035
Variance0.050279149
MonotonicityNot monotonic
2025-11-08T09:12:16.799353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
012089
36.8%
0.12430
 
0.1%
0.0010630
 
0.1%
1.16 × 10-626
 
0.1%
1.21 × 10-626
 
0.1%
1.17 × 10-523
 
0.1%
0.0001623
 
0.1%
0.00011522
 
0.1%
1.85 × 10-522
 
0.1%
0.011422
 
0.1%
Other values (4719)20520
62.5%
ValueCountFrequency (%)
012089
36.8%
1 × 10-65
 
< 0.1%
1.01 × 10-617
 
0.1%
1.02 × 10-67
 
< 0.1%
1.03 × 10-614
 
< 0.1%
1.04 × 10-620
 
0.1%
1.05 × 10-69
 
< 0.1%
1.06 × 10-610
 
< 0.1%
1.07 × 10-613
 
< 0.1%
1.08 × 10-613
 
< 0.1%
ValueCountFrequency (%)
0.9942
< 0.1%
0.9871
 
< 0.1%
0.9831
 
< 0.1%
0.9821
 
< 0.1%
0.9811
 
< 0.1%
0.9791
 
< 0.1%
0.9742
< 0.1%
0.9723
< 0.1%
0.9712
< 0.1%
0.971
 
< 0.1%

liveness
Real number (ℝ)

Distinct1624
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1901762
Minimum0
Maximum0.996
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2025-11-08T09:12:17.004181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0559
Q10.0927
median0.127
Q30.248
95-th percentile0.5104
Maximum0.996
Range0.996
Interquartile range (IQR)0.1553

Descriptive statistics

Standard deviation0.15431728
Coefficient of variation (CV)0.81144372
Kurtosis5.065937
Mean0.1901762
Median Absolute Deviation (MAD)0.0496
Skewness2.0767204
Sum6244.055
Variance0.023813823
MonotonicityNot monotonic
2025-11-08T09:12:17.212432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111346
 
1.1%
0.108310
 
0.9%
0.11305
 
0.9%
0.105295
 
0.9%
0.104294
 
0.9%
0.109287
 
0.9%
0.106284
 
0.9%
0.101275
 
0.8%
0.112272
 
0.8%
0.107266
 
0.8%
Other values (1614)29899
91.1%
ValueCountFrequency (%)
01
< 0.1%
0.009361
< 0.1%
0.009461
< 0.1%
0.01311
< 0.1%
0.0152
< 0.1%
0.01552
< 0.1%
0.01581
< 0.1%
0.01631
< 0.1%
0.01651
< 0.1%
0.01671
< 0.1%
ValueCountFrequency (%)
0.9961
 
< 0.1%
0.9941
 
< 0.1%
0.9921
 
< 0.1%
0.9912
 
< 0.1%
0.993
< 0.1%
0.9885
< 0.1%
0.9853
< 0.1%
0.9841
 
< 0.1%
0.9832
 
< 0.1%
0.9821
 
< 0.1%

valence
Real number (ℝ)

Distinct1362
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51056097
Minimum0
Maximum0.991
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2025-11-08T09:12:17.427156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.132
Q10.331
median0.512
Q30.693
95-th percentile0.893
Maximum0.991
Range0.991
Interquartile range (IQR)0.362

Descriptive statistics

Standard deviation0.23314597
Coefficient of variation (CV)0.45664668
Kurtosis-0.90098076
Mean0.51056097
Median Absolute Deviation (MAD)0.181
Skewness-0.0054853502
Sum16763.248
Variance0.054357045
MonotonicityNot monotonic
2025-11-08T09:12:17.641771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.96169
 
0.2%
0.5868
 
0.2%
0.38968
 
0.2%
0.49968
 
0.2%
0.56268
 
0.2%
0.4368
 
0.2%
0.51667
 
0.2%
0.34766
 
0.2%
0.39266
 
0.2%
0.53666
 
0.2%
Other values (1352)32159
97.9%
ValueCountFrequency (%)
01
 
< 0.1%
1 × 10-55
< 0.1%
0.01161
 
< 0.1%
0.01221
 
< 0.1%
0.01391
 
< 0.1%
0.01591
 
< 0.1%
0.02231
 
< 0.1%
0.02341
 
< 0.1%
0.02691
 
< 0.1%
0.02761
 
< 0.1%
ValueCountFrequency (%)
0.9911
 
< 0.1%
0.991
 
< 0.1%
0.9851
 
< 0.1%
0.9841
 
< 0.1%
0.9831
 
< 0.1%
0.9812
 
< 0.1%
0.981
 
< 0.1%
0.9793
< 0.1%
0.9781
 
< 0.1%
0.9775
< 0.1%

tempo
Real number (ℝ)

Distinct17684
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.88113
Minimum0
Maximum239.44
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2025-11-08T09:12:17.871775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile81.0682
Q199.96
median121.984
Q3133.918
95-th percentile173.95
Maximum239.44
Range239.44
Interquartile range (IQR)33.958

Descriptive statistics

Standard deviation26.903624
Coefficient of variation (CV)0.22256264
Kurtosis0.08326436
Mean120.88113
Median Absolute Deviation (MAD)18.045
Skewness0.52887789
Sum3968890.2
Variance723.80499
MonotonicityNot monotonic
2025-11-08T09:12:18.099267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.99245
 
0.1%
127.99435
 
0.1%
127.99333
 
0.1%
128.00732
 
0.1%
127.99731
 
0.1%
128.00331
 
0.1%
128.00131
 
0.1%
128.00530
 
0.1%
128.01729
 
0.1%
127.99129
 
0.1%
Other values (17674)32507
99.0%
ValueCountFrequency (%)
01
< 0.1%
35.4771
< 0.1%
37.1141
< 0.1%
38.9851
< 0.1%
46.1691
< 0.1%
48.7182
< 0.1%
48.9811
< 0.1%
49.5971
< 0.1%
50.4541
< 0.1%
52.0171
< 0.1%
ValueCountFrequency (%)
239.441
< 0.1%
220.2521
< 0.1%
219.9911
< 0.1%
219.9611
< 0.1%
214.5161
< 0.1%
214.0471
< 0.1%
214.0171
< 0.1%
213.991
< 0.1%
212.1372
< 0.1%
212.0581
< 0.1%

duration_ms
Real number (ℝ)

Distinct19785
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225799.81
Minimum4000
Maximum517810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size256.6 KiB
2025-11-08T09:12:18.321645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile148394.8
Q1187819
median216000
Q3253585
95-th percentile337400
Maximum517810
Range513810
Interquartile range (IQR)65766

Descriptive statistics

Standard deviation59834.006
Coefficient of variation (CV)0.26498696
Kurtosis2.6991863
Mean225799.81
Median Absolute Deviation (MAD)31867
Skewness1.1498633
Sum7.4136852 × 109
Variance3.5801083 × 109
MonotonicityNot monotonic
2025-11-08T09:12:18.572579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19200037
 
0.1%
24000037
 
0.1%
21000030
 
0.1%
18000026
 
0.1%
19500025
 
0.1%
16000024
 
0.1%
22500023
 
0.1%
20300019
 
0.1%
18800018
 
0.1%
16800018
 
0.1%
Other values (19775)32576
99.2%
ValueCountFrequency (%)
40001
< 0.1%
294931
< 0.1%
314291
< 0.1%
318751
< 0.1%
318931
< 0.1%
337502
< 0.1%
339001
< 0.1%
345601
< 0.1%
375001
< 0.1%
376401
< 0.1%
ValueCountFrequency (%)
5178101
< 0.1%
5171252
< 0.1%
5168931
< 0.1%
5167601
< 0.1%
5159601
< 0.1%
5158672
< 0.1%
5157031
< 0.1%
5156801
< 0.1%
5134401
< 0.1%
5130001
< 0.1%

release_year
Real number (ℝ)

Missing 

Distinct61
Distinct (%)0.2%
Missing1886
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2012.2005
Minimum1957
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.7 KiB
2025-11-08T09:12:18.817919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1957
5-th percentile1988
Q12010
median2017
Q32019
95-th percentile2019
Maximum2020
Range63
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.397763
Coefficient of variation (CV)0.0051673591
Kurtosis4.3423172
Mean2012.2005
Median Absolute Deviation (MAD)2
Skewness-2.1311641
Sum62271570
Variance108.11347
MonotonicityNot monotonic
2025-11-08T09:12:19.001365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20199079
27.7%
20183312
 
10.1%
20172426
 
7.4%
20162114
 
6.4%
20151761
 
5.4%
20141505
 
4.6%
2013921
 
2.8%
2020785
 
2.4%
2012737
 
2.2%
2008576
 
1.8%
Other values (51)7731
23.5%
(Missing)1886
 
5.7%
ValueCountFrequency (%)
19571
 
< 0.1%
19581
 
< 0.1%
19611
 
< 0.1%
19634
 
< 0.1%
19648
 
< 0.1%
196510
 
< 0.1%
196614
 
< 0.1%
196730
0.1%
196816
 
< 0.1%
196948
0.1%
ValueCountFrequency (%)
2020785
 
2.4%
20199079
27.7%
20183312
 
10.1%
20172426
 
7.4%
20162114
 
6.4%
20151761
 
5.4%
20141505
 
4.6%
2013921
 
2.8%
2012737
 
2.2%
2011558
 
1.7%

release_month
Real number (ℝ)

Missing 

Distinct12
Distinct (%)< 0.1%
Missing1886
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean6.4538728
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.7 KiB
2025-11-08T09:12:19.114955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7274881
Coefficient of variation (CV)0.57755835
Kurtosis-1.3495395
Mean6.4538728
Median Absolute Deviation (MAD)3
Skewness-0.10342503
Sum199728
Variance13.894167
MonotonicityNot monotonic
2025-11-08T09:12:19.215725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
15092
15.5%
113359
10.2%
103086
9.4%
92532
7.7%
122444
7.4%
62390
7.3%
82298
7.0%
52213
6.7%
32011
 
6.1%
72009
 
6.1%
Other values (2)3513
10.7%
ValueCountFrequency (%)
15092
15.5%
21613
 
4.9%
32011
 
6.1%
41900
 
5.8%
52213
6.7%
62390
7.3%
72009
 
6.1%
82298
7.0%
92532
7.7%
103086
9.4%
ValueCountFrequency (%)
122444
7.4%
113359
10.2%
103086
9.4%
92532
7.7%
82298
7.0%
72009
6.1%
62390
7.3%
52213
6.7%
41900
5.8%
32011
6.1%

Interactions

2025-11-08T09:12:03.759955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:38.805378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:40.427812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:42.314792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:43.915229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:45.915788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:47.869310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:50.226998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:52.530779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:54.122487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:55.765261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:57.374008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:59.507462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:12:01.299891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:12:03.924778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:38.915879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:40.554978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:42.426998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:44.024224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:46.027261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:48.031920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:50.408426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:52.643475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:54.238451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:55.877764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:57.485046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:59.635623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:12:01.469912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:12:04.104558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:39.031011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:40.667450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:42.534311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:44.141164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:46.136253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:48.195377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:50.582236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:52.749879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:54.351629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:55.984310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:58.139602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:59.749043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:12:01.649580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:12:04.288690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:39.151052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-08T09:11:40.781299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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Correlations

2025-11-08T09:12:19.326251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodeplaylist_genreplaylist_subgenrerelease_monthrelease_yearspeechinesstempotrack_popularityvalence
acousticness1.0000.104-0.075-0.491-0.2140.007-0.058-0.2840.0300.1300.1350.0290.0720.029-0.1670.1290.089
danceability0.1041.000-0.091-0.142-0.0560.014-0.138-0.0230.0610.2160.1740.0350.1210.261-0.1680.0650.331
duration_ms-0.075-0.0911.000-0.0110.0780.013-0.040-0.1330.0280.1420.164-0.110-0.446-0.125-0.016-0.121-0.004
energy-0.491-0.142-0.0111.0000.1150.0090.1400.6560.0260.1870.177-0.034-0.0280.0650.177-0.1180.123
instrumentalness-0.214-0.0560.0780.1151.0000.012-0.029-0.1620.0050.1410.1420.013-0.029-0.1990.071-0.193-0.160
key0.0070.0140.0130.0090.0121.0000.000-0.0040.3020.0620.0530.0110.0030.029-0.016-0.0010.018
liveness-0.058-0.138-0.0400.140-0.0290.0001.0000.0830.0130.0520.063-0.006-0.0010.0570.033-0.030-0.054
loudness-0.284-0.023-0.1330.656-0.162-0.0040.0831.0000.0150.1240.1280.0100.1550.1000.1120.0650.042
mode0.0300.0610.0280.0260.0050.3020.0130.0151.0000.1250.1500.0270.0880.0660.0300.0290.000
playlist_genre0.1300.2160.1420.1870.1410.0620.0520.1240.1251.0001.0000.0530.2610.2020.2040.1080.127
playlist_subgenre0.1350.1740.1640.1770.1420.0530.0630.1280.1501.0001.0000.0720.2730.1650.1780.1500.124
release_month0.0290.035-0.110-0.0340.0130.011-0.0060.0100.0270.0530.0721.0000.1820.0210.0120.076-0.059
release_year0.0720.121-0.446-0.028-0.0290.003-0.0010.1550.0880.2610.2730.1821.0000.1260.0420.181-0.177
speechiness0.0290.261-0.1250.065-0.1990.0290.0570.1000.0660.2020.1650.0210.1261.0000.0260.0070.078
tempo-0.167-0.168-0.0160.1770.071-0.0160.0330.1120.0300.2040.1780.0120.0420.0261.000-0.022-0.063
track_popularity0.1290.065-0.121-0.118-0.193-0.001-0.0300.0650.0290.1080.1500.0760.1810.007-0.0221.0000.037
valence0.0890.331-0.0040.123-0.1600.018-0.0540.0420.0000.1270.124-0.059-0.1770.078-0.0630.0371.000

Missing values

2025-11-08T09:12:05.904580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-08T09:12:06.218358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-08T09:12:07.173224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

track_idtrack_nametrack_artisttrack_popularitytrack_album_idtrack_album_nametrack_album_release_dateplaylist_nameplaylist_idplaylist_genreplaylist_subgenredanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_msrelease_yearrelease_month
06f807x0ima9a1j3VPbc7VNi dont care with justin bieber loud luxury remixed sheeran662oCs0DGTsRO98Gh5ZSl2Cxi dont care with justin bieber loud luxury remix2019-06-14pop remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.7480.9166-2.63410.05830.10200.0000000.06530.518122.03619475420196
10r7CVbZTWZgbTCYdfa2P31memories dillon francis remixmaroon 56763rPSO264uRjW1X5E6cWv6memories dillon francis remix2019-12-13pop remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.7260.81511-4.96910.03730.07240.0042100.35700.69399.972162600201912
21z1Hg7Vb0AhHDiEmnDE79lall the time don diablo remixzara larsson701HoSmj2eLcsrR0vE9gThr4all the time don diablo remix2019-07-05pop remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.6750.9311-3.43200.07420.07940.0000230.11000.613124.00817661620197
375FpbthrwQmzHlBJLuGdC7call you mine keanu silva remixthe chainsmokers601nqYsOef1yKKuGOVchbsk6call you mine the remixes2019-07-19pop remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.7180.9307-3.77810.10200.02870.0000090.20400.277121.95616909320197
41e8PAfcKUYoKkxPhrHqw4xsomeone you loved future humans remixlewis capaldi697m7vv9wlQ4i0LFuJiE2zsQsomeone you loved future humans remix2019-03-05pop remix37i9dQZF1DXcZDD7cfEKhWpopdance pop0.6500.8331-4.67210.03590.08030.0000000.08330.725123.97618905220193
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328230coMLoVcagZPGF5zxc5RF8everybody is in the place radio edithardwell281PdMbB6qgSzS9zcT9xP6Kxeverybody is in the place radio edit2014-04-18edm love 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.6090.9892-3.51010.08670.0004340.2190000.07150.0358130.04617169720144
328243zKST4nk4QJE77oLjUZ0Nghey brotheravicii2002h9kO2oLKnLtycgbElKswtrue2013-01-01edm love 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5450.7807-4.86700.04360.0309000.0000460.08280.4580125.01425509320131
328252EpS5TgdngSISM63rhBsnKbooyah radio editshowtek470Dix8CfvtZEHUyJGnmPnaBbooyah2013-01-01edm love 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5590.91611-3.05010.06260.0453000.0000130.22500.1950128.01221529520131
328261EavLSmwRWtmkKEmlCfFzTwastedtisto47584m4QL0kmpG69zSpMKvv8wasted2014-04-22edm love 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.6450.8322-5.59510.02940.0010600.0026400.19900.3750112.02818837120144
328270aBDrRTgDCwWbcOnEIp7DJmany ways radio editother2759XOfNjuYZB6feC6QUzS3emany waysNaTedm love 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5810.6405-8.36710.03650.0266000.0000000.57200.2880128.001196993<NA><NA>
328287bxnKAamR3snQ1VGLuVfC1city of lights official radio editlush simon422azRoBBWEEEYhqV6sb7JrTcity of lights vocal mix2014-04-28edm love 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.4280.9222-1.81410.09360.0766000.0000000.06680.2100128.17020437520144
328295Aevni09Em4575077nkWHzcloser sultan ned shepard remixother206kD6KLxj7s8eCE3ABvAyf5closer remixed2013-03-08edm love 20206jI1gFr6ANFtT8MmTvA2Uxedmprogressive electro house0.5220.7860-4.46210.04200.0017100.0042700.37500.4000128.04135312020133
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